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Nuclear Genome Sequence Survey of the Dinoflagellate <i>Heterocapsa triquetra</i>

2008· article· en· W2083054468 on OpenAlexafffund
Michelle Louise McEwan, RAHEEL HUMAYUN, Claudio H. Slamovits, Patrick J. Keeling

Bibliographic record

VenueJournal of Eukaryotic Microbiology · 2008
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicProtist diversity and phylogeny
Canadian institutionsUniversity of British ColumbiaCanadian Institute for Advanced Research
FundersMichael Smith Health Research BCTula Foundation
KeywordsDinoflagellateBiologyGenomePseudogeneEvolutionary biologySequence (biology)Genome sizeGeneticsRepeated sequenceDNA sequencingGeneEcology

Abstract

fetched live from OpenAlex

Dinoflagellates have among the largest nuclear genomes known, but we know little about their contents or organisation. Given the interest in dinoflagellate ecology, cell biology, and evolutionary biology, there are many reasons to thoroughly investigate the contents of dinoflagellate genomes, but because of their large size the only thorough samples to date have relied on expressed sequence tag surveys to analyse cDNAs. To complement this, there are some studies of the physical properties of dinoflagellate chromosomes, but no direct survey of the nature of the sequences contained within them. To start to build a picture of the contents of these genomes, we have sequenced over 230,000 bp from the nuclear genome of Heterocapsa triquetra, which has been estimated to be 18-23 billion base pairs in total. The survey includes one putative gene with two relict spliced leaders, one putative pseudogene, and a small number of low-complexity repeats, transposons, and other putative selfish elements, all of which account for about 5% of the survey. Another 5% of the survey was long, complex repeats, some highly represented. By far the greatest fraction of the survey (89.5%) is made up of non-repeated sequence with no similarity to any other known sequence.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.631
Threshold uncertainty score0.362

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.027
GPT teacher head0.218
Teacher spread0.191 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations49
Published2008
Admission routes2
Has abstractyes

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